Textual Machine Learning: An Application to Computational Economics Research

We demonstrate the benefit to economics of machine learning approaches for textual analysis. Our use case is a machine learning based structuring of research on computational economics based on 1160 articles published in the Computational Economics journal from 1993 to 2019. Our Latent Dirichlet Allocation approach, popular in the computer sciences, use a probabilistic approach to identify shared topics across a body of documents. This combines natural language processing of article content with probabilistic learning of the latent (hidden) topics that link groups of articles. We show that this body of research can be well-described by 18 overall topics and provide a structure for computational economists to adopt this approach in other avenues.